scholarly journals PREDIKSI TINGKAT KEKERUHAN (TURBIDITAS) MENGGUNAKAN CITRA SATELIT SENTINEL-2A DI WADUK JATILUHUR, JAWA BARAT

2021 ◽  
Vol 17 (2) ◽  
pp. 59-68
Author(s):  
Arip Rahman ◽  
Lismining Pujiyani Astuti ◽  
Andri Warsa ◽  
Agus Arifin Sentosa
Keyword(s):  
Remote Sensing ◽  
Satellite Imagery ◽  
Physical Characteristics ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Data Measurement ◽  
Reservoir Siltation ◽  
Sentinel 2A ◽  
Imagery Data

Turbidity is one of the remote sensing indicators on the  reservoir physical characteristics that can reduce its brightness level. Measuring reservoir physical characteristics traditionally are expensive and time consuming as well. Therefore, remote sensing is used as an alternative for turbidity measurement because it can provide data and products spatially, temporally as well as synoptically with low cost. This study aims to obtain an algorithm using a combination of in-situ turbidity data measurement and Sentinel-2A satellite imagery data. The resulting algorithm can be used to predict and map turbidity in Jatiluhur Reservoir. Based on the multiregression between B3 (green band) and B4 (red band) with in-situ turbidity data measurement, it is obtainted that the regression coefficients are a = 76.77, b = 63.22 and c = -34.31 respectively, with the equation of Y = 76, 77+63.22 X1-34.31X2 (Y=predicted turbidity, X1=lnB3, X2=lnB4). The correlation value between in situ and turbidity prediction is quite strong with a coefficient of determination (R2) of 0.60, and Root Mean Square Error (RMSE) of 1.95 NTU. Based on Mean Absolute Percentage Error (MAPE) analysis, the deviation is 31.1%. High levels of turbidity can reduce the main productivity of water and its organisms, especially in respiratory and visual problems. Sedimentation caused by high turbidity levels can make siltation which results in reservoir capacity loss.Keywords: Turbidity, remote sensing, Sentinel-2A satellite imagery data, Jatiluhur Reservoir, siltation

scholarly journals KLASIFIKASI HABITAT PERAIRAN DANGKAL MENGGUNAKAN LOGIKA FUZZY DAN MAXIMUM LIKELIHOOD PADA CITRA SATELIT MULTISPEKTRAL

2018 ◽  
Vol 10 (3) ◽  
pp. 667-681
Author(s):  
Muhammad Siddiq Sangadji ◽  
Vincentius Paulus Siregar ◽  
Henry Munandar Manik
Keyword(s):  
Remote Sensing ◽  
Fuzzy Logic ◽  
Maximum Likelihood ◽  
Satellite Imagery ◽  
Data Retrieval ◽  
Classification Algorithm ◽  
Benthic Habitat ◽  
Fuzzy Logic Algorithm ◽  
Accuracy Level ◽  
Sentinel 2A

ABSTRAKLogika fuzzy memiliki aplikasi di berbagai bidang, namun memiliki arti khusus untuk penginderaan jarak jauh. Logika fuzzy memungkinkan keanggotaan parsial, bagian yang sangat penting dibidang penginderaan jarak jauh, karena keanggotaan parsial diterjemahkan secara dekat dengan masalah piksel campuran. Penelitian ini bertujuan untuk menerapkan algoritma klasifikasi logika fuzzy untuk memetakan habitat dasar Perairan dangkal pada Citra Satelit SPOT 7 dan Sentinel 2A, menguji tingkat akurasinya dan membandingkan algoritma klasifikasi logika fuzzy dengan maximum likelihood. Pengambilan data lapang berlokasi di gusung Karang Lebar dan Karang Congkak, Kepuluan Seribu pada tanggal 6 Desember sampai dengan 10 Desember 2017. Keseluruhan hasil uji akurasi menunjukan bahwa algoritma logika fuzzy masih memiliki tingkat akurasi yang baik dibandingkan dengan algoritma maximum likelihood. Perbedaan ukuran pixel (resolusi spasial) dari citra satelit juga mempengaruhi hasil akurasi, dimana citra satelit SPOT 7 memiliki tingkat akurasi yang lebih besar dibandingkan dengan Sentinel 2A.ABSTRACTFuzzy logic has applications in various fields, but has special meaning for remote sensing. Fuzzy logic allows partial membership, a very important property in the field of remote sensing, since partial membership is translated closely to the problem of mixed pixels. The aim of this research is to apply fuzzy logic classification algorithm to map benthic habitat in SPOT 7 and Sentinel 2A satellite imagery, test its accuracy level and compare fuzzy logic classification algorithm with maximum likelihood. Field data retrieval located in Karang Lebar and Karang Congkak, Kepulauan Seribu on 6 December until 10 December 2017. The overall accuracy test results show that fuzzy logic algorithm still has a good accuracy level compared to the maximum likelihood algorithm. Differences in pixel size (spatial resolution) of satellite imagery also affect accuracy results, where SPOT 7 satellite imagery has greater accuracy then Sentinel 2A. 

Sentinel-2 mission Contribution for Supporting Bathymetric layers of SAFAGA coastal zone, Egypt

2021 ◽  
Author(s):  
Ramez Saeed ◽  
Saad Abdelrahman ◽  
Andrea Scozari ◽  
Abdelazim Negm
Keyword(s):  
Red Sea ◽  
Satellite Imagery ◽  
Data Science ◽  
New Technologies ◽  
Coefficient Of Determination ◽  
Change Environment ◽  
Bathymetric Data ◽  
Work Supports ◽  
Sentinel 2

<p><strong>ABSTRACT</strong></p><p>With the fast and highly growing demand for all possible ways of remote work as a result of COVID19 pandemic, new technologies using Satellite data were highly encouraged for multidisciplinary applications in different fields such as; agriculture, climate change, environment, coastal management, maritime, security and Blue Economy.</p><p>This work supports applying Satellite Derived Bathymetry (SDB) with the available low-cost multispectral satellite imagery applications, instruments and readily accessible data for different areas with only their benthic parameters, water characteristics and atmospheric conditions.  The main goal of this work is to derive bathymetric data needed for different hydrographic applications, such as: nautical charting, coastal engineering, water quality monitoring, sediment movement monitoring and supporting both green carbon and marine data science.  Also, this work proposes and assesses a SDB procedure that makes use of publicly-available multispectral satellite images (Sentinel2 MSI) and applies algorithms available in the SNAP software package for extracting bathymetry and supporting bathymetric layers against highly expensive traditional in-situ hydrographic surveys. The procedure was applied at SAFAGA harbor area, located south of Hurghada at (26°44′N, 33°56′E), on the Egyptian Red Sea coast.  SAFAGA controls important maritime traffic line in Red Sea such as (Safaga – Deba, Saudi Arabia) maritime cruises.  SAFAGA depths change between 6 m to 22m surrounded by many shoal batches and confined waters that largely affect maritime safety of navigation.  Therefore, there is always a high demand for updated nautical charts which this work supports.  The outcome of this work provides and fulfils those demands with bathymetric layers data for the approach channel and harbour usage bands electronic nautical chart of SAFAGA with reasonable accuracies.  The coefficient of determination (R<sup>2</sup>) differs between 0.42 to 0.71 after applying water column correction by Lyzenga algorithm and deriving bathymetric data depending on reflectance /radiance of optical imagery collected by sentinel2 missions with in-situ depth data values relationship by Stumpf equation.  The adopted approach proved to give  highly reasonable results that could be used in nautical charts compilation. Similar methodologies could be applied to inland water bodies.  This study is part of the MSc Thesis of the first author and is in the framework of a bilateral project between ASRT of Egypt and CNR of Italy which is still running.</p><p><strong>Keywords: Algorithm, Bathymetry, Sentinel 2, nautical charting, Safaga port, satellite imagery, water depth, Egypt.</strong></p>

scholarly journals Hyperspectral Remote Sensing of Phytoplankton Species Composition Based on Transfer Learning

2019 ◽  
Vol 11 (17) ◽  
pp. 2001 ◽  
Author(s):  
Qing Zhu ◽  
Fang Shen ◽  
Pei Shang ◽  
Yanqun Pan ◽  
Mengyu Li
Keyword(s):  
Remote Sensing ◽  
Species Composition ◽  
Transfer Learning ◽  
Natural Waters ◽  
Hyperspectral Remote Sensing ◽  
Sensor Technology ◽  
Percentage Error ◽  
Phytoplankton Species ◽  
Phytoplankton Species Composition

Phytoplankton species composition research is key to understanding phytoplankton ecological and biogeochemical functions. Hyperspectral optical sensor technology allows us to obtain detailed information about phytoplankton species composition. In the present study, a transfer learning method to inverse phytoplankton species composition using in situ hyperspectral remote sensing reflectance and hyperspectral satellite imagery was presented. By transferring the general knowledge learned from the first few layers of a deep neural network (DNN) trained by a general simulation dataset, and updating the last few layers with an in situ dataset, the requirement for large numbers of in situ samples for training the DNN to predict phytoplankton species composition in natural waters was lowered. This method was established from in situ datasets and validated with datasets collected in different ocean regions in China with considerable accuracy (R2 = 0.88, mean absolute percentage error (MAPE) = 26.08%). Application of the method to Hyperspectral Imager for the Coastal Ocean (HICO) imagery showed that spatial distributions of dominant phytoplankton species and associated compositions could be derived. These results indicated the feasibility of species composition inversion from hyperspectral remote sensing, highlighting the advantages of transfer learning algorithms, which can bring broader application prospects for phytoplankton species composition and phytoplankton functional type research.

scholarly journals Using Multi-Temporal MODIS NDVI Data to Monitor Tea Status and Forecast Yield: A Case Study at Tanuyen, Laichau, Vietnam

2020 ◽  
Vol 12 (11) ◽  
pp. 1814
Author(s):  
Phamchimai Phan ◽  
Nengcheng Chen ◽  
Lei Xu ◽  
Zeqiang Chen
Keyword(s):  
Remote Sensing ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Vegetation Index ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Support Vector ◽  
Mean Square ◽  
Multi Temporal

Tea is a cash crop that improves the quality of life for people in the Tanuyen District of Laichau Province, Vietnam. Tea yield, however, has stagnated in recent years, due to changes in temperature, precipitation, the age of the tea bushes, and diseases. Developing an approach for monitoring tea bushes by remote sensing and Geographic Information Systems (GIS) might be a way to alleviate this problem. Using multi-temporal remote sensing data, the paper details an investigation of the changes in tea health and yield forecasting through the normalized difference vegetation index (NDVI). In this study, we used NDVI as a support tool to demonstrate the temporal and spatial changes in NDVI through the extract tea NDVI value and calculate the mean NDVI value. The results of the study showed that the minimum NDVI value was 0.42 during January 2013 and February 2015 and 2016. The maximum NDVI value was in August 2015 and June 2017. We indicate that the linear relationship between NDVI value and mean temperature was strong with R 2 = 0.79 Our results confirm that the combination of meteorological data and NDVI data can achieve a high performance of yield prediction. Three models to predict tea yield were conducted: support vector machine (SVM), random forest (RF), and the traditional linear regression model (TLRM). For period 2009 to 2018, the prediction tea yield by the RF model was the best with a R 2 = 0.73 , by SVM it was 0.66, and 0.57 with the TLRM. Three evaluation indicators were used to consider accuracy: the coefficient of determination ( R 2 ), root-mean-square error (RMSE), and percentage error of tea yield (PETY). The highest accuracy for the three models was in 2015 with a R 2 ≥ 0.87, RMSE < 50 kg/ha, and PETY less 3% error. In the other years, the prediction accuracy was higher in the SVM and RF models. Meanwhile, the RF algorithm was better than PETY (≤10%) and the root mean square error for this algorithm was significantly less (≤80 kg/ha). RMSE and PETY showed relatively good values in the TLRM model with a RMSE from 80 to 100 kg/ha and a PETY from 8 to 15%.

scholarly journals Coral reef monitoring in Panjang Island, Central Bangka

2021 ◽  
Vol 926 (1) ◽  
pp. 012099
Author(s):  
W Adi ◽  
I Akhrianti ◽  
M Hudatwi
Keyword(s):  
Remote Sensing ◽  
Coral Reef ◽  
Coral Reefs ◽  
Satellite Imagery ◽  
Good Condition ◽  
Aerial Photographs ◽  
Visual Interpretation ◽  
Composite Band ◽  
Sentinel 2A ◽  
Color Composite

Abstract Bangka Island is the largest tin producer in Indonesia and since the granting of tin mining freedom in 2000, unconventional tin mining (TI) is increasingly prevalent. The existence of mining activities will directly or indirectly damage the environment both on land and at sea. Especially the high biodiversity of coral reef ecosystem. The purpose of this research was to analyze a map of the distribution of coral reef based on Sentinel 2A satellite imagery data. Analyze the extent of the coral reefs in shallow waters of Putri Island, and analyze of the condition coral reefs (percentage cover, mortality index and genus diversity) with using collaboration betwen the coral diving data and remote sensing data. Studies of changes in coral reef ecosystems have been ongoing since several decades ago. The combination of satellite imagery and aerial photographs is capable of making long-term and continuous observations on mapping and change detection. Remote sensing technology has several advantages overconventional sampling to monitor a large area in time almost simultaneously and continuously including the difficult to explore areas. This research was conducted with visual interpretation by using standard true color composite band (483) and false color composite band (843) of Sentinel 2A and also using lyzenga transformation. Estimation of coral reefs area based on result is 475,96 ha (2016) and decreased to 475 ha (2021). The condition of coral reefs at the research location is a good condition.

scholarly journals BATHYMETRY EXTRACTION FROM SPOT 7 SATELLITE IMAGERY USING RANDOM FOREST METHODS

2019 ◽  
Vol 16 (1) ◽  
pp. 23
Author(s):  
Kuncoro Teguh Setiawan ◽  
Nana Suwargana ◽  
Devica Natalia Br. Ginting ◽  
Masita Dwi Mandini Manessa ◽  
Nanin Anggraini ◽  
...  
Keyword(s):  
Random Forest ◽  
Satellite Imagery ◽  
Coefficient Of Determination ◽  
Shallow Waters ◽  
Benthic Habitat ◽  
Benthic Habitats ◽  
Depth Data ◽  
Marine Habitats ◽  
Random Forest Method

The scope of this research is the application of the random forest method to SPOT 7 data to produce bathymetry information for shallow waters in Indonesia. The study aimed to analyze the effect of base objects in shallow marine habitats on estimating bathymetry from SPOT 7 satellite imagery. SPOT 7 satellite imagery of the shallow sea waters of Gili Matra, West Nusa Tenggara Province was used in this research. The estimation of bathymetry was carried out using two in-situ depth-data modifications, in the form of a random forest algorithm used both without and with benthic habitats (coral reefs, seagrass, macroalgae, and substrates). For bathymetry estimation from SPOT 7 data, the first modification (without benthic habitats) resulted in a 90.2% coefficient of determination (R2) and 1.57 RMSE, while the second modification (with benthic habitats) resulted in an 85.3% coefficient of determination (R2) and 2.48 RMSE. This research showed that the first modification achieved slightly better results than the second modification; thus, the benthic habitat did not significantly influence bathymetry estimation from SPOT 7 imagery.

scholarly journals Retrieval and Mapping of Soil Organic Carbon Using Sentinel-2A Spectral Images from Bare Cropland in Autumn

2021 ◽  
Vol 13 (6) ◽  
pp. 1072
Author(s):  
Ke Wang ◽  
Yanbing Qi ◽  
Wenjing Guo ◽  
Jielin Zhang ◽  
Qingrui Chang
Keyword(s):  
Remote Sensing ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Soil Properties ◽  
Spectral Reflectance ◽  
Prediction Models ◽  
Coefficient Of Determination ◽  
Support Vector ◽  
Fertility Level ◽  
Sentinel 2A

Soil is the largest carbon reservoir on the terrestrial surface. Soil organic carbon (SOC) not only regulates global climate change, but also indicates soil fertility level in croplands. SOC prediction based on remote sensing images has generated great interest in the research field of digital soil mapping. The short revisiting time and wide spectral bands available from Sentinel-2A (S2A) remote sensing data can provide a useful data resource for soil property prediction. However, dense soil surface coverage reduces the direct relationship between soil properties and S2A spectral reflectance such that it is difficult to achieve a successful SOC prediction model. Observations of bare cropland in autumn provide the possibility to establish accurate SOC retrieval models using the S2A super-spectral reflectance. Therefore, in this study, we collected 225 topsoil samples from bare cropland in autumn and measured the SOC content. We also obtained S2A spectral images of the western Guanzhong Plain, China. We established four SOC prediction models, including random forest (RF), support vector machine (SVM), partial least-squares regression (PLSR), and artificial neural network (ANN) based on 15 variables retrieved from the S2A images, and compared the prediction accuracy using RMSE (root mean square error), R2 (coefficient of determination), and RPD (ratio of performance to deviation). Based on the optimal model, the spatial distribution of SOC was mapped and analyzed. The results indicated that the inversion model with the RF algorithm achieved the highest accuracy, with an R2 of 0.8581, RPD of 2.1313, and RMSE of 1.07. The variables retrieved from the shortwave infrared (SWIR) bands (B11 and B12) usually had higher variable importance, except for the ANN model. SOC content mapped with the RF model gradually decreased with increasing distance from the Wei river, and values were higher in the west than in the east. These results matched the SOC distribution based on measurements at the sample sites. This research provides evidence that soil properties such as SOC can be retrieved and spatially mapped based on S2A images that are obtained from bare cropland in autumn.

scholarly journals Comparison of In Situ and Remote-Sensing Methods to Determine Turbidity and Concentration of Suspended Matter in the Estuary Zone of the Mzymta River, Black Sea

2021 ◽  
Vol 13 (1) ◽  
pp. 143
Author(s):  
Ksenia Nazirova ◽  
Yana Alferyeva ◽  
Olga Lavrova ◽  
Yuri Shur ◽  
Dmitry Soloviev ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Black Sea ◽  
Atmospheric Correction ◽  
Landsat 8 ◽  
Suspended Particulate Matter Concentration ◽  
Water Sampling ◽  
Study Region ◽  
Sentinel 2A ◽  
Remote Sensing Methods

The paper presents the results of a comparison of water turbidity and suspended particulate matter concentration (SPM) obtained from quasi-synchronous in situ and satellite remote-sensing data. Field measurements from a small boat were performed in April and May 2019, in the northeastern part of the Black Sea, in the mouth area of the Mzymta River. The measuring instruments and methods included a turbidity sensor mounted on a CTD (Conductivity, Temperature, Depth), probe, a portable turbidimeter, water sampling for further laboratory analysis and collecting meteorological information from boat and ground-based weather stations. Remote-sensing methods included turbidity and SPM estimation using the C2RCC (Case 2 Regional Coast Color) and Atmospheric correction for OLI ‘lite’ (ACOLITE) ACOLITE processors that were run on Landsat-8 Operational Land Imager (OLI) and Sentinel-2A/2B Multispectral Instrument (MSI) satellite data. The highest correlation between the satellite SPM and the water sampling SPM for the study area in conditions of spring flooding was achieved using C2RCC, but only for measurements undertaken almost synchronously with satellite imaging because of the high mobility of the Mzymta plume. Within the few hours when all the stations were completed, its boundary could shift considerably. The ACOLITE algorithms overestimated by 1.5 times the water sampling SPM in the low value range up to 15 g/m3. For SPM over 20–25 g/m3, a high correlation was observed both with the in situ measurements and the C2RCC results. It was demonstrated that quantitative turbidity and SPM values retrieved from Landsat-8 OLI and Sentinel-2A/2B MSI data can adequately reflect the real situation even using standard retrieval algorithms, not regional ones, provided the best suited algorithm is selected for the study region.

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